Globally local: Hyper-local modeling for accurate forecast of COVID-19.

ABSTRACT:

Importance

Assumption of a well-mixed population during modeling is often erroneously made without due analysis of its validity. Ignoring the importance of the geo-spatial granularity at which the data is collected could have significant implications on the quality of forecasts and the actionable clinical recommendations that are based on it.

Objective

This paper's primary objective is to test the hypothesis that the characteristic dynamics defining the trajectory of the pandemic in a region is lost when the data is aggregated and modeled at higher geo-spatial levels.

Design

We use publicly available confirmed SARS-CoV-2 cases and deaths from January 1st, 2020 to August 3rd, 2020 in the United States at different geo-spatial granularities to conduct our experiments. T

SUBMITTER: Gopalakrishnan V 

PROVIDER: S-EPMC8518202 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature